According to the IDC, seventy-five percent of senior execs surveyed reported that poor document-driven business processes exposed their companies to needless risk, including lost customers.
With increased complexity of business processes and regulation—and compliance more stringent than ever—businesses must ensure that they have a holistic view of their document-driven processes and are compliant with regulatory standards.

In order to accelerate innovation and learning, the data science team at Uber is looking to optimize Driver, Rider, Eater, Restaurant and Courier experience through reinforcement learning methods.

The team has implemented bandits methods of optimization which learn iteratively and rapidly from a continuous evaluation of related metric performance. Recently, we completed an AI-powered experiment using bandits techniques for content optimization to improve the customer engagement. The technique helped improve customer experience compared to any classic hypothesis testing methods.

In this session, we will explain various use cases at Uber that this technique has proven its value and how bandits have helped optimize and improve customer experience and engagement at Uber.

Today’s ultra low-power sensors and wireless modules not only allow batteries to last longer but also make completely self-sustaining IoT devices possible. This webinar will overview energy harvesting methods including photovoltaic, piezoelectric, thermoelectric, and RF with a focus on indoor ambient light collection.

Practical applications and how to integrate solar energy harvesters into electronics will also be discussed. An indoor solar-powered Bluetooth sensor will be analyzed as a use case example.

Artificial Intelligence is a part of our daily lives through the use of technologies like virtual assistants such as Cortana, smart homes, and automated customer service. We also have the power of the Internet of Things technology in organizations. How can we put them together for success in our organizations?

Businesses are running the Red Queen's race not just to win, but to survive in a world where Artificial Intelligence and IoT are becoming the present as well as the future of technology, and ideas are developing into reality at accelerated rates.

How can you help your company to evolve, adapt and succeed using IoT and Artificial Intelligence to stay at the forefront of the competition, and win the Red Queen's Race? What are the potential issues, complications, and benefits that the future of technology could bring to us and our organisations, right now?

In this session, Jen Stirrup will explain the quick wins to win the Red Queen's Race in AI and IoT to help you and your organization to win the Red Queen's race.

In this session, learn how the revolution in event-driven application platforms enable innovative companies to develop, deploy and run real-time enterprise applications with dramatically reduced time-to-market, significantly lowered development and maintenance costs, and maximized agility in the face of requirements for continuous innovation and digital transformation.

At most organizations, conference rooms are less than perfect--and that affects the productivity of any meeting. Inconsistency is the cornerstone of meeting room space, whether it's the power outlet locations, wired or wireless connectivity, brand of digital whiteboard, or video conferencing capabilities. That's why many companies are investing in "Conference Room of the Future" initiatives.

Nemertes recently conducted research among 600+ global organizations, and this webinar will focus on best practices.

It will address the following:
• What technologies are crucial to effective meetings?
• Who should run the meeting-room overhauls for the most success --IT or facilities?
• What are the differences between smaller, huddle rooms and large conference rooms, in terms of user expectations?
• How do advanced technologies, such as artificial intelligence and Internet of Things, play a role in meeting rooms?
• What should IT leaders look for in management and monitoring tools?

We look forward to sharing this information with you and answering your questions!

With the General Data Protection Regulation (GDPR) becoming enforceable in the EU on May 25, 2018, many data scientists are worried about the impact that this regulation and similar initiatives in other countries that give consumers a "right to explanation" of decisions made by algorithms will have on the field of predictive and prescriptive analytics.

In this session, Beau will discuss the role of interpretable algorithms in data science as well as explore tools and methods for explaining high-performing algorithms.

Beau Walker has a Juris Doctorate (law degree) and BS and MS Degrees in Biology and Ecology and Evolution. Beau has worked in many domains including academia, pharma, healthcare, life sciences, insurance, legal, financial services, marketing, and IoT.

Implementing AI applications based on machine learning is a significant topic for organizations embracing digital transformation. By 2020, 30% of CIOs will include AI in their top five investment priorities according to Gartner’s Top 10 Strategic Technology Trends for 2018: Intelligent Apps and Analytics. But to deliver on the AI promise, organizations need to generate good quality data to train the algorithms. Failure to do so will result in the following scenario: "When you automate a mess, you get an automated mess."

This webinar covers:

- An introduction to machine learning use cases and challenges provided by Kirk Borne, Principal Data Scientist at Booz Allen Hamilton and top data science and big data influencer.
- How to achieve good data quality based on harmonized semantic metadata presented by Andreas Blumauer, CEO and co-founder of Semantic Web Company and a pioneer in the application of semantic web standards for enterprise data integration.
- How to apply a combined approach when semantic knowledge models and machine learning build the basis of your cognitive computing. (See Attachment: The Knowledge Graph as the Default Data Model for Machine Learning)
- Why a combination of machine and human computation approaches is required, not only from an ethical but also from a technical perspective.

In this webinar, Metadata.io CEO Gil Allouche will talk about the different ways AI is being used by marketers. From analyzing data to orchestrating new marketing campaigns, AI is powering marketing activities in new and exciting ways and affecting interactions throughout the entire customer lifecycle. As an example of how AI can have a tremendous impact on marketing practices, Gil will focus on its role in lead generation. Webinar attendees will learn:

- What Machine Learning is in relation to AI and how it connects your data to find patterns
- Examples of how machine learning can identify target audiences, including the 20 percent that creates 80 percent of your revenue
- How AI technology can help marketers prioritize their budgets to focus on the most effective programs
- Starting with small, iterative uses of AI in marketing can be the most effective way to understand what will yield the most ROI

Gil Allouche founded Metadata.io to make demand generation easy for non-technical marketers. The Metadata.io platform and AI Operator evolved from Gil's experiences hacking various marketing and CRM systems to get the solutions he needed.

Emerging trends in AI and Machine Learning will impact nearly every aspect of the way organizations create and capture value in the years to come. A new set of opportunities and threats will emerge and change the landscape of many industries.

Join IRI and RTI as we interview Sam Adams, IBM Distinguished Engineer, IBM Research, to learn more about emerging trends in the fields of AI and Machine Learning and the ways organizations can prepare for an Intelligent Future.

Artificial Intelligence, Machine Learning, Virtual Reality, and intelligent voice assistants offer the potential to drastically improve how people work with data and collaboration systems. In this webinar we'll share research on how organizations are:
• Evaluating and adopting AI and related technologies
• Building business cases to justify investment
• Measuring and monitoring success

We'll look at specific examples of AI technologies in the context of communications and collaboration, and provide attendees with actionable guidance as they develop their own AI evaluation and implementation plans.

What are the top use cases in AI? Or should we say, what will be the top use cases in the next 5-10 years? How are companies using the disciplines of Machine Learning, Deep Learning and HPC? What is needed to enable faster adoption? What technology changes are expected or required? Evaluator Group and Tractica, leading analyst firms on the topic of Artificial Intelligence come together to discuss these topics of market adoption and technology requirements.

This is an INTERACTIVE session. We will start with short presentations then open up the Fireside chat. The audience is invited to submit their questions and participate in what will be a lively discussion.

AI is a powerful tool, but often companies get more excited about their technology than in the customer value they’re creating. Geordie Kaytes will share a framework for building customer-centered AI products. You’ll learn how to craft a far-reaching vision and strategy centered around customer needs and balance that vision with the day-to-day needs of your company.

Learn a framework for creating and communicating a vision that describes the overall direction of your AI product, a defined product strategy, a cross-functional roadmap aligned with the strategy, and a list of metrics that track progress towards the strategy

About the Speaker: Geordie Kaytes is the director of UX strategy for Boston-area UI/UX studio Fresh Tilled Soil and a partner at Heroic (https://www.heroicteam.com), a design leadership coaching firm that helps growing companies scale their digital product capabilities. A digital product design leader with deep experience in design process transformation and cross-functional expertise in design, strategy, and technology, Geordie has helped companies in a broad range of industries develop a 360-degree view of their product design processes. Previously, he did his obligatory tour of duty in management consulting. He holds a BA from Yale in political science. He is a coauthor of the Medium publication Radical Product.

As we move to the conversational UI and take advantage of NLP and AI in general, we change the way we interact with technology dramatically. The standard GUI is many times fully eliminated, leading to novel challenges in UX. Tasks are removed from the user’s oversight with invisible or seamless software, and the output is not always as expected. But sometimes that output is correct within the parameters given and simply perceived as an error.

Dennis will talk through where x.ai has encountered error perception issues as we seek to develop frictionless software, how we thought about the problem and the communication strategies we’re exploring to resolve it.

We are seeing a sea change in networking. SDN has enabled improvements in network telemetry and analytics.

In this presentation, I will talk about the current challenges that are out there and how the technology change is helping us to improve the overall network telemetry. Furthermore, I will share how deep learning techniques are being used in this field. Please join this webinar to understand how the field of network telemetry is changing.

This Webinar explains how Big Data, Artificial Intelligence, and Machine Learning is going to transform the future Banking Industry. Banks which can manage this Big Data evolution successfully will survive and thrive, and give a more holistic and personalized customer service, thereby increasing their revenues tremendously.

The key takeaway from this Webinar is that “Right information at the right place and the right time is going to be the real money and will shape the future of Banking Industry.”

Tariq is a Fintech Expert, writer, and thinker based in Toronto Canada and is currently working on an initiative to disrupt the conventional Banking Industry with “Big Data Predictive Analytics Model” of his startup.

In this webinar, Mustafa Kabul, Principal Data Scientist, SAS, will provide an introduction to deep learning and its applications.

Mustafa is a data scientist in the Artificial Intelligence and Machine Learning R&D at SAS, where he leads innovative projects for SAS’s next-generation AI-enabled analytics products, including applications of deep learning. His current focus is on applying deep reinforcement learning to operational problems in the CRM and IoT spaces. An operations research expert working at the interface of machine learning and optimization, previously, he developed distributed, large-scale integer optimization algorithms for marketing optimization problems. Ever the optimization enthusiast, Mustafa always looks into ways to improve the algorithms. Nowadays his favorites are the distributed stochastic gradient and online learning methods. Mustafa holds a PhD from the University of North Carolina at Chapel Hill, where his research focused on game theory models of supply chains selling to strategic customers.

Andy Kriebel, Head Coach and Tableau Zen Master at The Data School & Eva Murray, Head of BI and Tableau Zen Master at Exasol

This webinar is part of BrightTALK's Founders Spotlight series, featuring fearless entrepreneurs and inspiring founders.

In this episode, Eva Murray & Andy Kriebel, Founders of Makeover Monday, will share their story of how they started the social data project, Makeover Monday, the challenges and successes they encountered along the way and how they overcame them.

This will be an interactive Q&A session and an excellent opportunity for entrepreneurs or professionals to have their questions answered.

This talk tells the story of implementation and optimization of a sparse logistic regression algorithm in spark. I would like to share the lessons I learned and the steps I had to take to improve the speed of execution and convergence of my initial naive implementation. The message isn’t to convince the audience that logistic regression is great and my implementation is awesome, rather it will give details about how it works under the hood, and general tips for implementing an iterative parallel machine learning algorithm in spark.

The talk is structured as a sequence of “lessons learned” that are shown in form of code examples building on the initial naive implementation. The performance impact of each “lesson” on execution time and speed of convergence is measured on benchmark datasets.

You will see how to formulate logistic regression in a parallel setting, how to avoid data shuffles, when to use a custom partitioner, how to use the ‘aggregate’ and ‘treeAggregate’ functions, how momentum can accelerate the convergence of gradient descent, and much more. I will assume basic understanding of machine learning and some prior knowledge of spark. The code examples are written in scala, and the code will be made available for each step in the walkthrough.

Lorand is a data scientist working on risk management and fraud prevention for the payment processing system of Zalando, the leading fashion platform in Europe. Previously, Lorand has developed highly scalable low-latency machine learning algorithms for real-time bidding in online advertising.

You can do a lot with a Raspberry and ASF projects. From a tiny object
connected to the internet to a small server application. The presentation
will explain and demo the following:

- Raspberry as small server and captive portal using httpd/tomcat.
- Raspberry as a IoT Sensor collecting data and sending it to ActiveMQ.
- Raspberry as a Modbus supervisor controlling an Industruino
(Industrial Arduino) and connected to ActiveMQ.

The 10x growth of transaction volumes, 50x growth in data volumes and drive for real-time visibility and responsiveness over the last decade have pushed traditional technologies including databases beyond their limits. Your choices are either buy expensive hardware to accelerate the wrong architecture, or do what other companies have started to do and invest in technologies being used for modern hybrid transactional analytical applications (HTAP).

Learn some of the current best practices in building HTAP applications, and the differences between two of the more common technologies companies use: Apache® Cassandra™ and Apache® Ignite™. This session will cover:

- The requirements for real-time, high volume HTAP applications
- Architectural best practices, including how in-memory computing fits in and has eliminated tradeoffs between consistency, speed and scale
- A detailed comparison of Apache Ignite and GridGain® for HTAP applications

About the speaker: Denis Magda is the Director of Product Management at GridGain Systems, and Vice President of the Apache Ignite PMC. He is an expert in distributed systems and platforms who actively contributes to Apache Ignite and helps companies and individuals deploy it for mission-critical applications. You can be sure to come across Denis at conferences, workshop and other events sharing his knowledge about use case, best practices, and implementation tips and tricks on how to build efficient applications with in-memory data grids, distributed databases and in-memory computing platforms including Apache Ignite and GridGain.

Before joining GridGain and becoming a part of Apache Ignite community, Denis worked for Oracle where he led the Java ME Embedded Porting Team -- helping bring Java to IoT.

Data is the foundation of any organization and therefore, it is paramount that it is managed and maintained as a valuable resource.

Subscribe to this channel to learn best practices and emerging trends in a variety of topics including data governance, analysis, quality management, warehousing, business intelligence, ERP, CRM, big data and more.